Predictive analytics is helping businesses in fields as diverse as healthcare, retailing, hospitality and insurance peer into the future to optimize inventories, manage staffing, enhance customer engagement, set pricing and achieve many other profit-building goals.

Thanks to steady advances in artificial intelligence (AI) and machine learning (ML), predictive analytics is growing increasingly accurate and insightful. Yet many businesses continue to view predictive analytics with various degrees of skepticism, convinced that the technology remains too complex, too disruptive and too expensive to incorporate into routine use.

While doubters can be firm in their beliefs, one thing they often fail to recognize is that predictive analytics is a tool that's best applied, at least initially, in small measures. "Getting started in predictive analytics is a lot like learning to swim," explains Ellen Houston, applied data science lead at Civis Analytics, an Eric Schmidt-backed data science software and consultancy firm founded in 2013 by Dan Wagner, chief analytics officer for President Obama's 2012 re-election campaign. "If you dive directly into the deep end, you may not make it very far," she quips.

Sriram Parthasarathy, senior director of predictive analytics for Logi Analytics, a predictive analytics platform provider, suggests that skeptics get their feet wet by using the technology to find the answer to just a single predictive question, utilizing historical data that's readily available. "Once you've started demonstrating the ROI of answering that problem, you can over time add more data to improve the model and incorporate new insights into other parts of the business workflow," he says. "Success with these initiatives will provide your business a competitively differentiated application and will drive more revenue."

Here’s a guide on how to get started with predictive analytics at your organization, from the ground up.